These five digital learning platforms (DLPs) have the power of scale: ASSISTment’s E-TRIALS, MATHia/UpGrade, Canvas + Terracotta, Kinetic by OpenStax, and ASU Learning @ Scale each have at least 100,000 users. Read on for an overview of each DLP:
We have 6,000 problems with more than one support for each one and they have been randomized to over 300 students each. Educational data scientists might ask questions about the features of these supports (i.e., are hints that are shorter correlated with better student learning). Learning scientists will ask questions that contrast specific features of interventions they are interested in exploring.
UpGrade is an open-source A/B testing platform that facilitates randomized experiments on digital learning experiences. Currently it allows experiments to contrast the type/sequence/timing of secondary math content within MATHia, but by the end of the grant period it will enable connections to other software applications.
Terracotta is a Canvas plug-in that allows teachers and researchers to embed studies directly in their learning management system course sites. The platform facilitates randomized experiments in service of evaluating the content, context, timing, and mode of learning activities. It includes an automated informed consent workflow, outcomes-mapping, and data export. The learning activities manipulated in Terracotta have the potential to be designed and implemented differentially at the class-level.
Kinetic will enable research on a wide spectrum of postsecondary learner outcomes related to behavior, performance, and psychosocial constructs. Depending upon the version of Kinetic and the exact outcomes of interest, researchers might design their studies using pre- and post-intervention assessments, collection of self-report or external data from students, and/or longitudinal analyses over multiple time scales using student records in OpenStax products. The alpha version will allow researchers to administer any measure that can be delivered via Qualtrics, while the beta version will allow linking of these researcher-administered measures to existing learner outcomes in OpenStax materials.
The L@S data warehouse will allow researchers to conduct several types of exploratory analysis and future designs may allow experimental interventions.
This project is supported by the Institute of Education Sciences, U.S. Department of Education, through Grant R305N210034 to Digital Promise. The opinions expressed are those of the authors and do not represent views of the Institute or the U.S. Department of Education.